AI-Enabled Retail Loss Prevention: A Framework for Enhanced Security and Operational Efficiency
Year: 2025
Type: article
Abstract: This article examines the transformative role of artificial intelligence in modernizing retail loss prevention strategies. The article presents a comprehensive analysis of AI-driven technologies, including machine learning algorithms, computer vision systems, and IoT integration, in detecting and preventing retail shrinkage. Through systematic review and industry case analyses, the article explores how these technologies enhance traditional loss prevention methods while addressing implementation challenges, ethical considerations, and privacy concerns. The article demonstrates that AI-powered solutions offer retailers advanced capabilities in real-time monitoring, predictive analytics, and automated response systems, leading to more effective loss prevention outcomes. The article also identifies key factors for successful implementation and provides a framework for retailers to evaluate and deploy AI-based security solutions while maintaining customer privacy and regulatory compliance. This article contributes to the growing body of knowledge on retail technology integration and offers practical insights for industry practitioners seeking to enhance their loss prevention capabilities through AI adoption. Additionally, the article outlines future research directions and emerging trends in retail security technology, emphasizing the importance of industry collaboration and standardization efforts.
Author: Durga Rao Manchikanti
Institution: Target (United States)
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4407601620